► On scanning the brain, in order to understand its ability, to process patterns of information.

A gentle and relaxing introduction to the art and science of understanding- pattern matching as it pertains to the human brain and mind.

♦ To scan the brain is to look at all parts of the brain carefully in order to detect it's features.

We should first remind ourselves that scanning is not just about the machines commonly referred to as scanners - in this context we are looking at the brain with a high degree of scrutiny to reveal its architecture. We have the intention of building a sophisticated mechanism with which to understand the brains ability to empower the mind with the capacity to perform pattern matching subconsciously. Extrapolations have been previously made to help us arrive at our current understanding of the brain. Philosophically, we have been asking many questions about the brain for a long time.

Cognitive science seeks to unify neuroscience and psychology with other fields that concern themselves with the brain, such as computer science (artificial intelligence and similar fields) and philosophy. The oldest method of studying the brain is anatomical, and until the middle of the 20th century, much of the progress in neuroscience came from the development of better cell stains and better microscopes. Computational neuroscience encompasses two approaches: first, the use of computers to study the brain; second, the study of how brains perform computation.

On one hand, it is possible to write a computer program to simulate the operation of a group of neurons by making use of systems of equations that describe their electrochemical activity; such simulations are known as biologically realistic neural networks. On the other hand, it is possible to study algorithms for neural computation by simulating, or mathematically analyzing, the operations of simplified "units" that have some of the properties of neurons but abstract out much of their biological complexity. The computational functions of the brain are studied both by computer scientists and neuroscientists.

Hippocrates, On the Sacred Disease wrote:Men ought to know that from nothing else but the brain come joys, delights, laughter and sports, and sorrows, griefs, despondency, and lamentations. ... And by the same organ we become mad and delirious, and fears and terrors assail us, some by night, and some by day, and dreams and untimely wanderings, and cares that are not suitable, and ignorance of present circumstances, desuetude, and unskillfulness. All these things we endure from the brain, when it is not healthy...

By using previous data, whether written or graphical in nature, we are able to enhance our exploration to uncover many of the brains still hidden secrets. We are able to make many conclusions by looking for correlations in available data against our own theories, ideas and thoughts. We are also able to create metadata that can be graphed for further visual reference(we can call these graphs, meta-graphs). The output from the machines that we refer to as scanners, and the meta-graphs that we create can be collectively referred to as scans.

Andreas Vesalius (31 December 1514 – 15 October 1564) was a 16th-century Flemish/Netherlandish anatomist, physician, and author of one of the most influential books on human anatomy, De humani corporis fabrica (On the Fabric of the Human Body). Vesalius is often referred to as the founder of modern human anatomy.

A quick scan(careful look) of these two images, reveals the basal ganglia and some history.

In vertebrates, the reward-punishment system is implemented by a specific set of brain structures, at the heart of which lie the basal ganglia, a set of interconnected areas at the base of the forebrain. There is much reward for understanding how the brain gives the subconscious the ability for pattern matching even though somtimes the effort can be rather punishing. The subconscious I believe has an intimate connection to the brain . . .

What I know, I take for granted. Up until now I have been trying to illustrate an answer to your question which turns out has some difficulty associated with providing an answer. Hopefully I can further improve on this for the time being. Then I can add to it later.

In the next part we will take a brief look at pattern matching but for now let us continue with this part . . .. . . This part is going to give us a few hints. This post is no exception . . .

First let us quickly examine the neocortex. We are talking about scanning the brain and we must understand how we arrived at current day methods and why those methods are improving all of the time. Some of the methods currently not publicly available are quite sophisticated compared to those that are public.

gib wrote:it does not yet get into how to answer the question: how do we scan the brain to find pattern recognition? By pattern recognition, we are talking about how the mind recognizes objects or properties or events based on how well it matches similar patterns from past experience. What would we be looking at in the brain--via an fMRI scan, for example--such that we could say: ah, the brain is recognizing a pattern in its sensory input.

First we must understand that scanning is not just about technology - in this context we are looking at something with care to detect a feature. There is quite a bit of inference going on to say the least - to say this is done without errors is quite silly. I believe the inference is quite accurate and the computer models show this to be the case as I will demonstrate.

This will take a little time to develop, sink in and make sense.

encode_decode wrote:The inference is made on the following: Cutting up the neocortex - delightful, brain scans(neuroimaging) - there are at least ten we could choose from. Interestingly the idea of neuroimaging goes back a long way and its life actually starts out in blood circulation over 120 years ago but anyway. PET and fMRI scans are very useful. EEG has added much data despite its spatial limitations - there is no substitute for cutting the brain up. Obviously microscopes(optical and electron based) give plenty of visual data.

We were able to guess at what the brain was doing before we made many attempts at breaking it down further. With the results from our thoughts we started looking for things that may have not been there but in many cases were.

Now we are able to get many high resolution photographs from microscopy.

Microscopy is the technical field of using microscopes to view objects and areas of objects that cannot be seen with the naked eye (objects that are not within the resolution range of the normal eye). There are three well-known branches of microscopy: optical, electron, and scanning probe microscopy.

However we still find use of illustrations and diagrams ever important and perhaps . . .. . . these are more important for making guesses before developing the technology.

Illustrations, diagrams, graphs and textual data. We will first take a look at a few illustrations . . .

The neocortex, also called the neopallium and isocortex, is the part of the mammalian brain involved in higher-order brain functions such as sensory perception, cognition, generation of motor commands, spatial reasoning and language. This illustration is where we need to start paying attention:

Here we are looking at the six layers of the neocortex that I mentioned before. The different cortical layers each contain a characteristic distribution of neuronal cell types and connections with other cortical and subcortical regions. There are direct connections between different cortical areas and indirect connections via the thalamus, for example. The thalamus has multiple functions. It may be thought of as a kind of hub of information.[clarification needed] It is generally believed to act as a relay between different subcortical areas and the cerebral cortex. The cerebral cortex can be classified into two parts, the large area of neocortex and the much smaller area of allocortex - we are examining the neocortex.

Here we are looking at abstract connections between the thalamus and the multiple planar(grid) layers of the neocortex. In case you are wondering whether there is a missing dimension in my post - fear not - it is a trick of the mind - I can explain further.

We have three new dimensions(grids(planar layers), columns) to work on top of the regular three dimensions(x,y,z) plus one of time(vicinity) making seven dimensions. Let us make more sense of these dimensions as follows:

Spatial Dimension - X

Spatial Dimension - Y

Spatial Dimension - Z

Grids

Columns

Layers

Times

By using the spatial dimensions and time we can create 3D pictures of a pattern at any given moment. We can then infer what the grids, columns and layers are doing. This all works in reverse and is just simplified for the sake of our discussion. Each of the layers have two distinct dimensions.

The previous explanations are somewhat simplified, and I may have even added an extra dimension - but we can improve on accuracy here.

We can simplify the neural connections for a computer model by adding extra planar layers - each containing two dimensions and then we stack them to make a third dimension, finishing with time as our reference point and a fourth dimension. Each planar layer becomes an analogy of sorts.

In the middle are the planar layers and to the right is a representation that one atom on the grid is a neuron.

This image is from a company called Numenta who are quite advanced with their research.

The thing to note is the stack of planar layers.

This is a different way of looking at the same thing. Here we are using analogies between layers as I will demonstrate in the next illustration.

Notice that Cat and Dog are the most similar followed by Cat and Fish for what ever reason.

Take note that the neocortex is looking for similarities between events - these are analogies and vicinities as previously discussed. Side on we can start looking at it a different way - as is illustrated in the following image:

This is a side view - we are looking at some stacked layers side on. Making connections that can be imaged.

This is where the multidimensional network starts to produce patterns - not yet optimized but yet sufficient to understanding.

Now we can start some imaging - false color type in the next image:

With all of this in mind we can go further to create spatial patterns that can be compared to each other - thereby giving all the x,y,z,t connections that are taking place - on a live scanner one would notice that thoughts produce distinct 3D patterns > that can be superimposed onto brain scans - some of which will be 3D.

From here we move on to develop theories of how to make all of this happen - we return to inference which is of course a conclusion reached on the basis of evidence and reasoning. Using the current scanning techniques along side our theories we can narrow down the field of information we are looking for.

Each time we narrow down the field of accuracy in the information we can inversely increase the resolution of scans.

A brief tour of stuff from computer science related to the brain, mind and cognition.

ForwardI want to point out that imagination has to be used alongside scanning to make leaps of understanding how the brain and mind process patterns in nature. As can be inferred from what we have already covered, these days there is an intimate connection between people and technology when it comes to understanding the brain and mind. Following are brief introductions into Pattern Matching, Pattern Recognition, Statistical Inference, Computer Vision, Speech Recognition(Hearing) and Hierarchical Temporal Memory. What we will do with these introductions is to mash them together into an insightful theory of how they are related to the brains ability to endow the mind with the capacity to recognize patterns by virtue of analogy and vicinity.

Pattern MatchingIn computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact. The patterns generally have the form of either sequences or tree structures. Uses of pattern matching include outputting the locations (if any) of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence (i.e., search and replace).

Pattern RecognitionPattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).

Statistical InferenceStatistical inference is the process of deducing properties of an underlying probability distribution by analysis of data. Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. The population is assumed to be larger than the observed data set; in other words, the observed data is assumed to be sampled from a larger population.

VisionComputer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

HearingSpeech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as "automatic speech recognition" (ASR), "computer speech recognition", or just "speech to text" (STT). It incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields. Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker independent" systems. Systems that use training are called "speaker dependent".

Hierarchical Temporal MemoryHierarchical temporal memory (HTM) is a biologically constrained theory of machine intelligence originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee. HTM is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the human brain. At the core of HTM are learning algorithms that can store, learn, infer and recall high-order sequences. Unlike most other machine learning methods, HTM learns time-based patterns in unlabeled data on a continuous basis. HTM is robust to noise and high capacity, meaning that it can learn multiple patterns simultaneously. When applied to computers, HTM is well suited for prediction, anomaly detection, classification and ultimately sensorimotor applications

Keep in mind that all inputs are encoded for the machine and all human inputs are also encoded.

It is this encoding that shows me the mind is indeed much different to the brain . . .. . . and the minds thoughts must be translated many times for the brain . . .. . . to process - but neither the mind or brain understand each other . . .

Pattern recognition in the brain is less tolerant than the minds abilities - much like hardware versus software.

I am hoping you can see that the ball/wall system is much different to the brain compared to the mind if you have not already seen that before I came along - that is going to make it easier. The sensation, that is, mind and brain are the same, is fallacious.

This sensation that I have mentioned is not well thought out in some science while slightly less than half have an idea of what I am speaking of.

Not many consider the connection you and I have made between computers and brain - software and mind - et cetera . . .

Let us begin to make the connections between these patterns - perhaps you will know before the scanner comes into being- that the scanner must first learn its patient before it can scan accurately - not the chicken or egg.

Arcturus Descending wrote:I like to express it by saying that the brain is the flower and the mind is its scent in a manner of speaking.

You know, that is a great description and it highlights how the mind is more tolerant than the brain when it begins to mold itself around such description. Emotion arises within because of a rational mismatch - this mismatch exerts chemical influence back into the body - what goes up must come down - every time.

Without such release the brain can never win as much as with only such release.

It reminds me of the mood pond being influenced by emotional ripples . . .. . . eventually coming home to the whole when the waves reach the shores . . .

You know that time when you cannot put into words a feeling that you have, that you just know it is there? Sometimes it is a bad feeling and at other times it is a good feeling. Your brain and your mind are arguing over some sort of pattern - a good suggestion that the subconscious exists.

That something between the mind and body is in mismatch - that something is a pattern of course and it gives to your conscious a sensation.

First of all I am not going to give up on this - because I can see it clearly, I need to learn how to put my visualization into words, and once I do this we can be excited, and along the way I have been discovering a few new things - I am going with science is what we know, and philosophy is to discover what we don't.

This post here--good intro--but it does not yet get into how to answer the question: This in itself has provided me with such a challenge and has left me wondering why I can not answer this in any simple way - it is time for me to analyze and review so as to give me a clue to why answering is difficult. Consider this part one of my review - I hope some sense comes from it.

Method

Identifying the source

How do we scan the brain to find pattern recognition? We do not scan the brain right now because our technology is not powerful enough to give us a clear enough picture but we have much evidence to say that what we are looking at is accurate enough to infer the next step - by the time I have approached this several more times from other angles it should become clear that in fact we can create a scanner to find pattern recognition - this time I use the sprinkler as an example.

Try not to think too much about the sprinkler itself - just think about the pattern the water droplets make as they leave the sprinkler and also realize that this pattern has a source(where the sprinkler is located). We can do the same with the brain - we can say that when a person thinks of a cat then a certain part of the brain is the source of that thought. So even though our tech is not powerful enough to give us an exact picture we can move from the source into more abstract notions.

AnalogyBy pattern recognition, we are talking about how the mind recognizes objects or properties or events based on how well it matches similar patterns from past experience. By pattern recognition we are talking about my principle of analogy and what that entails - that a cat and a dog has four legs says that the cat and the dog are more closely related than the fish but things could be connected in commonalities with fish if two entities ate fish then they are closer because of the fish.

Graph the sourceWhat would we be looking at in the brain--via an fMRI scan, for example--such that we could say: ah, the brain is recognizing a pattern in its sensory input? The fMRI is not a powerful enough scan to give us an absolutely clear enough picture of recognition taking place inside the brain - then there is the problem of mind - mind being the software running atop the hardware is processing information using a different language than the brain - so hence we have an issue that must be first worked out regarding the difference between the brain and mind. The fMRI can only help us get at the source - graph the source.